Genome-wide association studies for feedlot and growth traits in cattle1

Abstract

ABSTRACT:A genome wide-association study for production traits in cattle was carried out using genotype data from the 10K Affymetrix (Santa Clara, CA) and the 50K Illumina (San Diego, CA) SNP chips. The results for residual feed intake (RFI), BW, and hip height in 3 beef breed types (Bos indicus, Bos taurus, and B. indicus × B. taurus), and for stature in dairy cattle, are presented. The aims were to discover SNP associated with all traits studied, but especially RFI, and further to test the consistency of SNP effects across different cattle populations and breed types. The data were analyzed within data sets and within breed types by using a mixed model and fitting 1 SNP at a time. In each case, the number of significant SNP was more than expected by chance alone. A total of 75 SNP from the reference population with 50K chip data were significant (P < 0.001) for RFI, with a false discovery rate of 68%. These 75 SNP were mapped on 24 different BTA. Of the 75 SNP, the 9 most significant SNP were detected on BTA 3, 5, 7, and 8, with P ≤ 6.0 × 10 −5 . In a population of Angus cattle divergently selected for high and low RFI and 10K chip data, 111 SNP were significantly (P < 0.001) associated with RFI, with a false discovery rate of 7%. Approximately 103 of these SNP were therefore likely to represent true positives. Because of the small number of SNP common to both the 10K and 50K SNP chips, only 27 SNP were significantly (P < 0.05) associated with RFI in the 2 populations. However, other chromosome regions were found that contained SNP significantly associated with RFI in both data sets, although no SNP within the region showed a consistent effect on RFI. The SNP effects were consistent between data sets only when estimated within the same breed type.

Molecular value predictions: Associations with beef quality, carcass, production, behavior, and efficiency phenotypes in Brahman cattle1

“…The MVP for RFI was not related to RFI or FCR in the Brahman cattle in this experiment (Table 11). This is consistent with failure of feed efficiency markers to validate across different cattle breeds (Johnston and Graser, 2010;Bolormaa et al, 2011a;Littlejohn et al, 2012), with the RFI MVP explaining only 1% of phenotypic variation in RFI in purebred Bos indicus cattle and less than 1% of phenotypic variation in Bos taurus and Bos indicus × Bos taurus crossbred cattle (Johnston and Graser, 2009). This is despite reports of multiple markers explaining a large proportion of genetic variation in several other studies (Moore et al, 2009).…”

“…The development of improved MVP for feed efficiency and other traits will require large-scale discovery and validation of SNP from genome-wide association studies (Bolormaa et al, 2011a(Bolormaa et al, , 2011b(Bolormaa et al, , 2013Eggen, 2012) and SNP panels, which are becoming increasingly available commercially, with more gene markers contributing to genotypic and phenotypic variation. Their utility would be enhanced by the development of phenotyping systems with more efficient, higher throughput and standardized phenotyping, particularly for difficult to measure traits, such as cooked beef palatability and FCR, that allow for deeper biological phenotyping, enabling meaningful biological phenotypes to contribute to commercial traits (Hocquette et al, 2012;Pollak et al, 2012).…”

Genetic architecture of body size in mammals

“…Association studies in livestock generally have small sample sizes (approximately 2,000 records), and hence relatively high false discovery rates compared with GWASs in humans (for example, [30]). This means that defining genes identified by livestock GWASs is more uncertain than those identified in humans.…”

PHYSIOLOGY AND ENDOCRINOLOGY SYMPOSIUM: How single nucleotide polymorphism chips will advance our knowledge of factors controlling puberty and aid in selecting replacement beef females1,2,3,4

“…Spacing of the 50K is sufficient to locate SNP that are in linkage disequilibrium (LD) with underlying QTL within cattle breeds, but inconsistent LD patterns among cattle breeds (Gautier et al, 2007;Bovine HapMap Consortium, 2009) indicate that, across breeds, the same SNP from the 50K will not consistently be associated with the same underlying QTL. A comparison of 50K SNP effects on feed intake and efficiency estimated in Australian and US cattle showed that individual SNP effects were inconsistent but identified 1-Mb intervals containing SNP associated with the traits in each population (Bolormaa et al, 2011;Pollak et al, 2012). Higher marker density within these intervals might identify SNP having more consistent associations across populations of cattle due to stronger, more consistent LD between QTL and SNP with closer spacing between SNP.…”